def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# use --bmname=[dataset_name] for Reddit-Binary, Mutagenicity
if prog_args.bmname is not None:
benchmark_task(prog_args, writer=writer)
elif prog_args.pkl_fname is not None:
pkl_task(prog_args)
elif prog_args.dataset is not None:
if prog_args.dataset == "syn1":
syn_task1(prog_args, writer=writer)
elif prog_args.dataset == "syn2":
syn_task2(prog_args, writer=writer)
elif prog_args.dataset == "syn3":
syn_task3(prog_args, writer=writer)
elif prog_args.dataset == "syn4":
syn_task4(prog_args, writer=writer)
elif prog_args.dataset == "syn5":
syn_task5(prog_args, writer=writer)
elif prog_args.dataset == "enron":
enron_task(prog_args, writer=writer)
elif prog_args.dataset == "ppi_essential":
ppi_essential_task(prog_args, writer=writer)
writer.close()
def main():
args = configs.arg_parse()
fix_seed(args.seed)
node_indices = None
start_time = time.time()
if args.multiclass == False:
filter_useless_features(args.dataset, args.model, args.feat_explainers,
args.hops, args.num_samples, args.test_samples,
args.K, args.prop_noise_feat, node_indices,
args.info, args.hv, args.feat, args.coal,
args.g, args.multiclass, args.regu, args.gpu,
args.fullempty, args.S, args.seed)
else:
filter_useless_features_multiclass(
args.dataset, args.model, args.feat_explainers, args.hops,
args.num_samples, args.test_samples, args.prop_noise_feat,
node_indices, 5, args.info, args.hv, args.feat, args.coal, args.g,
args.multiclass, args.regu, args.gpu, args.fullempty, args.S,
args.seed)
end_time = time.time()
print('Time: ', end_time - start_time)
def main():
# Load a configuration
args = configs.arg_parse()
fix_seed(args.seed)
# GPU or CPU
if args.gpu:
print("CUDA")
else:
print("Using CPU")
# Load dataset
data = prepare_data(args.dataset, args.train_ratio, args.input_dim, args.seed)
# Load model
model_path = 'models/GCN_model_{}.pth'.format(args.dataset)
model = torch.load(model_path)
# Evaluate GraphSVX
if args.dataset == 'Mutagenicity':
data = selected_data(data, args.dataset)
eval_Mutagenicity(data, model, args)
elif args.dataset == 'syn6':
eval_syn6(data, model, args)
else:
eval_syn(data, model, args)
def main():
args = configs.arg_parse()
fix_seed(args.seed)
# Load the dataset
data = prepare_data(args.dataset, args.train_ratio, args.input_dim,
args.seed)
# Define and train the model
if args.dataset in ['Cora', 'PubMed']:
# Retrieve the model and training hyperparameters depending the data/model given as input
hyperparam = ''.join(['hparams_', args.dataset, '_', args.model])
param = ''.join(['params_', args.dataset, '_', args.model])
model = eval(args.model)(input_dim=data.num_features,
output_dim=data.num_classes,
**eval(hyperparam))
train_and_val(model, data, **eval(param))
_, test_acc = evaluate(data, model, data.test_mask)
print('Test accuracy is {:.4f}'.format(test_acc))
elif args.dataset in ['syn6', 'Mutagenicity']:
input_dims = data.x.shape[-1]
model = GcnEncoderGraph(input_dims,
args.hidden_dim,
args.output_dim,
data.num_classes,
args.num_gc_layers,
bn=args.bn,
dropout=args.dropout,
args=args)
train_gc(data, model, args)
_, test_acc = evaluate(data, model, data.test_mask)
print('Test accuracy is {:.4f}'.format(test_acc))
else:
# For pytorch geometric model
#model = GCNNet(args.input_dim, args.hidden_dim,
# data.num_classes, args.num_gc_layers, args=args)
input_dims = data.x.shape[-1]
model = GcnEncoderNode(data.num_features,
args.hidden_dim,
args.output_dim,
data.num_classes,
args.num_gc_layers,
bn=args.bn,
dropout=args.dropout,
args=args)
train_syn(data, model, args)
_, test_acc = evaluate(data, model, data.test_mask)
print('Test accuracy is {:.4f}'.format(test_acc))
# Save model
model_path = 'models/{}_model_{}.pth'.format(args.model, args.dataset)
if not os.path.exists(model_path) or args.save == True:
torch.save(model, model_path)
def main():
prog_args = configs.arg_parse()
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
return medic(prog_args)
def main():
args = configs.arg_parse()
fix_seed(args.seed)
# Load the dataset
data = prepare_data(args.dataset, args.train_ratio,
args.input_dim, args.seed)
# Load the model
model_path = 'models/{}_model_{}.pth'.format(args.model, args.dataset)
model = torch.load(model_path)
# Evaluate the model
if args.dataset in ['Cora', 'PubMed']:
_, test_acc = evaluate(data, model, data.test_mask)
else:
test_acc = test(data, model, data.test_mask)
print('Test accuracy is {:.4f}'.format(test_acc))
# Explain it with GraphSVX
explainer = GraphSVX(data, model, args.gpu)
# Distinguish graph classfication from node classification
if args.dataset in ['Mutagenicity', 'syn6']:
explanations = explainer.explain_graphs(args.indexes,
args.hops,
args.num_samples,
args.info,
args.multiclass,
args.fullempty,
args.S,
'graph_classification',
args.feat,
args.coal,
args.g,
args.regu,
True)
else:
explanations = explainer.explain(args.indexes,
args.hops,
args.num_samples,
args.info,
args.multiclass,
args.fullempty,
args.S,
args.hv,
args.feat,
args.coal,
args.g,
args.regu,
True)
print('Sum explanations: ', [np.sum(explanation) for explanation in explanations])
print('Base value: ', explainer.base_values)
def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
syn_task1(prog_args, 30, writer=writer)
syn_task2(prog_args, writer=writer)
syn_task1(prog_args, 300, writer=writer)
syn_task1(prog_args, 3000, writer=writer)
writer.close()
def main():
prog_args = configs.arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
writer = SummaryWriter(path)
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# use --bmname=[dataset_name] for Reddit-Binary, Mutagenicity
if prog_args.dataset == "FFMpeg":
FFMpeg(prog_args, writer=writer)
elif prog_args.dataset == "fan":
fan(prog_args)
elif prog_args.dataset == "reveal":
reveal(prog_args, writer=writer)
writer.close()
for epoch in range(args.num_epochs):
total_loss = 0
model.train()
for batch in loader:
# print ('batch:', batch.feat)
opt.zero_grad()
pred = model(batch)
pred = pred[train_mask]
# print ('pred:', pred)
label = labels[train_mask]
# print ('label:', label)
loss = model.loss(pred, label)
print('loss:', loss)
loss.backward()
opt.step()
total_loss += loss.item() * 1
total_loss /= num_train
writer.add_scalar("loss", total_loss, epoch)
if epoch % 10 == 0:
test_acc = test(loader, model, args, labels, test_mask)
print("Epoch {}. Loss: {:.4f}. Test accuracy: {:.4f}".format(
epoch, total_loss, test_acc))
writer.add_scalar("test accuracy", test_acc, epoch)
prog_args = arg_parse()
path = os.path.join(prog_args.logdir, io_utils.gen_prefix(prog_args))
syn_task1(prog_args, writer=SummaryWriter(path))
def get_ground_truth_syn4(node):
buff = node - 1
base = [0, 1, 2, 3, 4, 5]
ground_truth = []
offset = buff % 6
ground_truth = [buff - offset + val + 1 for val in base]
return ground_truth
def get_ground_truth_syn5(node):
base = [0, 1, 2, 3, 4, 5, 6, 7, 8]
buff = node - 7
ground_truth = []
offset = buff % 9
ground_truth = [buff - offset + val + 7 for val in base]
return ground_truth
# Get explanations
prog_args = configs.arg_parse()
savename = utils.gen_filesave(prog_args)
explanations = np.load(savename, allow_pickle='TRUE').item()
if prog_args.dataset is not None:
if prog_args.dataset == "bitcoinalpha":
evaluate_bitcoin_explanation(explanations, prog_args)
elif prog_args.dataset == "bitcoinotc":
evaluate_bitcoin_explanation(explanations, prog_args)
else:
evaluate_syn_explanation(explanations, prog_args)
